ABSTRACT
This chapter introduces types of variables that might be part of a statistical test, to introduce types of hypotheses, to give an overview of probability theory, discusses sampling distributions, and finally explores how those concepts are used in null hypothesis significance testing (NHST). In quantitative analysis, we must specify a hypothesis beforehand and then test our hypotheses using probability analyses. The specific kind of testing used in most (but not all) quantitative analysis is null hypothesis significance testing. The alternative hypothesis is the opposite of the null. Sometimes called the research hypothesis, this hypothesis will be that there is some difference or relationship. A Type I error occurs when we reject the null hypothesis, but it was actually correct. In other words, a Type I error is when we conclude there is a significant difference, but there is no real difference. A Type II error occurs when we conclude there is no significant difference, but there is actually a difference.
